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• 1. [Article] Hydrologic Remote Sensing and Land Surface Data Assimilation

  Accurate, reliable and skillful forecasting of key environmental variables such as soil moisture and snow are of paramount importance due to their strong influence on many water resources applications ...

  Citation × Citation Citation Abstract Copy Title: Hydrologic Remote Sensing and Land Surface Data Assimilation Author: Moradkhani, Hamid Year: 2008 Accurate, reliable and skillful forecasting of key environmental variables such as soil moisture and snow are of paramount importance due to their strong influence on many water resources applications including flood control, agricultural production and effective water resources management which collectively control the behavior of the climate system. Soil moisture is a key state variable in land surface?atmosphere interactions affecting surface energy fluxes, runoff and the radiation balance. Snow processes also have a large influence on land-atmosphere energy exchanges due to snow high albedo, low thermal conductivity and considerable spatial and temporal variability resulting in the dramatic change on surface and ground temperature. Measurement of these two variables is possible through variety of methods using ground-based and remote sensing procedures. Remote sensing, however, holds great promise for soil moisture and snow measurements which have considerable spatial and temporal variability. Merging these measurements with hydrologic model outputs in a systematic and effective way results in an improvement of land surface model prediction. Data Assimilation provides a mechanism to combine these two sources of estimation. Much success has been attained in recent years in using data from passive microwave sensors and assimilating them into the models. This paper provides an overview of the remote sensing measurement techniques for soil moisture and snow data and describes the advances in data assimilation techniques through the ensemble filtering, mainly Ensemble Kalman filter (EnKF) and Particle filter (PF), for improving the model prediction and reducing the uncertainties involved in prediction process. It is believed that PF provides a complete representation of the probability distribution of state variables of interests (according to sequential Bayes law) and could be a strong alternative to EnKF which is subject to some limitations including the linear updating rule and assumption of jointly normal distribution of errors in state variables and observation. Title: Hydrologic Remote Sensing and Land Surface Data Assimilation Author: Moradkhani, Hamid Year: 2008 Accurate, reliable and skillful forecasting of key environmental variables such as soil moisture and snow are of paramount importance due to their strong influence on many water resources applications including flood control, agricultural production and effective water resources management which collectively control the behavior of the climate system. Soil moisture is a key state variable in land surface?atmosphere interactions affecting surface energy fluxes, runoff and the radiation balance. Snow processes also have a large influence on land-atmosphere energy exchanges due to snow high albedo, low thermal conductivity and considerable spatial and temporal variability resulting in the dramatic change on surface and ground temperature. Measurement of these two variables is possible through variety of methods using ground-based and remote sensing procedures. Remote sensing, however, holds great promise for soil moisture and snow measurements which have considerable spatial and temporal variability. Merging these measurements with hydrologic model outputs in a systematic and effective way results in an improvement of land surface model prediction. Data Assimilation provides a mechanism to combine these two sources of estimation. Much success has been attained in recent years in using data from passive microwave sensors and assimilating them into the models. This paper provides an overview of the remote sensing measurement techniques for soil moisture and snow data and describes the advances in data assimilation techniques through the ensemble filtering, mainly Ensemble Kalman filter (EnKF) and Particle filter (PF), for improving the model prediction and reducing the uncertainties involved in prediction process. It is believed that PF provides a complete representation of the probability distribution of state variables of interests (according to sequential Bayes law) and could be a strong alternative to EnKF which is subject to some limitations including the linear updating rule and assumption of jointly normal distribution of errors in state variables and observation. Close

• 2. [Article] Improving Soil Moisture Profile Prediction With the Particle Filter-Markov Chain Monte Carlo Method

  Satellite soil moisture estimates have received increasing attention over the past decade. This paper examines the applicability of estimating soil moisture states and soil hydraulic parameters through ...

  Citation × Citation Citation Abstract Copy Title: Improving Soil Moisture Profile Prediction With the Particle Filter-Markov Chain Monte Carlo Method Author: Yan, Hongxiang, DeChant, Caleb Matthew, Moradkhani, Hamid Year: 2015 Satellite soil moisture estimates have received increasing attention over the past decade. This paper examines the applicability of estimating soil moisture states and soil hydraulic parameters through two particle filter (PF) methods: The PF with commonly used sampling importance resampling (PF-SIR) and the PF with recently developed Markov chain Monte Carlo sampling (PF-MCMC) methods. In a synthetic experiment, the potential of assimilating remotely sensed near-surface soil moisture measurements into a 1-D mechanistic soil water model (HYDRUS-1D) using both the PF-SIR and PF-MCMC algorithms is analyzed. The effects of satellite temporal resolution and accuracy, soil type, and ensemble size on the assimilation of soil moisture are analyzed. In a real data experiment, we first validate the Advanced Microwave Scanning Radiometer--Earth Observing System (AMSR-E) soil moisture products in the Oklahoma Little Washita Watershed. Aside from rescaling the remotely sensed soil moisture, a bias correction algorithm is implemented to correct the deep soil moisture estimate. Both the ascending and descending AMSR-E soil moisture data are assimilated into the HYDRUS-1D model. The synthetic assimilation results indicated that, whereas both updating schemes showed the ability to correct the soil moisture state and estimate hydraulic parameters, the PF-MCMC scheme is consistently more accurate than PR-SIR. For real data case, the quality of remotely sensed soil moisture impacts the benefits of their assimilation into the model. The PF-MCMC scheme brought marginal gains than the open-loop simulation in RMSE at both surface and root-zone soil layer, whereas the PF-SIR scheme degraded the open-loop simulation. Title: Improving Soil Moisture Profile Prediction With the Particle Filter-Markov Chain Monte Carlo Method Author: Yan, Hongxiang, DeChant, Caleb Matthew, Moradkhani, Hamid Year: 2015 Satellite soil moisture estimates have received increasing attention over the past decade. This paper examines the applicability of estimating soil moisture states and soil hydraulic parameters through two particle filter (PF) methods: The PF with commonly used sampling importance resampling (PF-SIR) and the PF with recently developed Markov chain Monte Carlo sampling (PF-MCMC) methods. In a synthetic experiment, the potential of assimilating remotely sensed near-surface soil moisture measurements into a 1-D mechanistic soil water model (HYDRUS-1D) using both the PF-SIR and PF-MCMC algorithms is analyzed. The effects of satellite temporal resolution and accuracy, soil type, and ensemble size on the assimilation of soil moisture are analyzed. In a real data experiment, we first validate the Advanced Microwave Scanning Radiometer--Earth Observing System (AMSR-E) soil moisture products in the Oklahoma Little Washita Watershed. Aside from rescaling the remotely sensed soil moisture, a bias correction algorithm is implemented to correct the deep soil moisture estimate. Both the ascending and descending AMSR-E soil moisture data are assimilated into the HYDRUS-1D model. The synthetic assimilation results indicated that, whereas both updating schemes showed the ability to correct the soil moisture state and estimate hydraulic parameters, the PF-MCMC scheme is consistently more accurate than PR-SIR. For real data case, the quality of remotely sensed soil moisture impacts the benefits of their assimilation into the model. The PF-MCMC scheme brought marginal gains than the open-loop simulation in RMSE at both surface and root-zone soil layer, whereas the PF-SIR scheme degraded the open-loop simulation. Close

• 3. [Article] Factors for Improved Fish Passage Waterway Construction

  Streambeds are important fish passageways in Oregon; they provide for the necessary habitats and spawning cycles of a healthy fish population. Oregon state law requires that hydraulic structures located ...

  Citation × Citation Citation Abstract Copy Title: Factors for Improved Fish Passage Waterway Construction Author: Sillars, David N., Moradkhani, Hamid, Tymvios, Nicholas, Smith, Trevor D. Year: 2011 Streambeds are important fish passageways in Oregon; they provide for the necessary habitats and spawning cycles of a healthy fish population. Oregon state law requires that hydraulic structures located in water properly provide fish passage. Increasingly stringent state and federal regulations apply to these fish passageways, and designers must become more cognizant of conditions over a range of flows to accommodate fish movement and avoid expensive structural failure of these passageways. Fish passage structures are built when roads cross streambeds and may include culverts, or bridges. When these structures are built, the streambeds are re-created using a technique called “roughened channels”. Roughened channels are man-made stream channels utilized for re-creating the hydraulics necessary for adequate stream passage, and this may include new constructions or retrofits of older, inadequate structures. Mixtures of materials are used to construct the bed of roughened channels, ranging from fines such as sand, silt and gravel to coarse elements like cobbles and boulders. Fines are a critical element in limiting permeability of the constructed bed thus keeping stream flow at the surface of the roughened channel during low flow periods. This report discusses work of a research project designed to discover factors that are key to successful long-term implementation of fish passageways, especially focused on the construction process. Areas of inquiry postulated in this study are that failures experienced in actual installations may be due to inadequate range and/or mix of soil and rock material gradation; unexpected water velocity, especially during high flows; inadequate mixing of rock and soil materials during construction; and inadequate compaction of rock and soil materials during construction. This report suggests that several factors may be especially important considerations in fish passage success. These factors are the relationship of downstream slope to structure slope, well-graded fine soil materials in the channel fill (improved by choice of fill source), and frequent site visits. Improving fish passages for cost-efficient fish movement is a priority for government agencies such as Oregon Department of Transportation (ODOT) and Oregon Transportation Research and Education Consortium (OTREC). Title: Factors for Improved Fish Passage Waterway Construction Author: Sillars, David N., Moradkhani, Hamid, Tymvios, Nicholas, Smith, Trevor D. Year: 2011 Streambeds are important fish passageways in Oregon; they provide for the necessary habitats and spawning cycles of a healthy fish population. Oregon state law requires that hydraulic structures located in water properly provide fish passage. Increasingly stringent state and federal regulations apply to these fish passageways, and designers must become more cognizant of conditions over a range of flows to accommodate fish movement and avoid expensive structural failure of these passageways. Fish passage structures are built when roads cross streambeds and may include culverts, or bridges. When these structures are built, the streambeds are re-created using a technique called “roughened channels”. Roughened channels are man-made stream channels utilized for re-creating the hydraulics necessary for adequate stream passage, and this may include new constructions or retrofits of older, inadequate structures. Mixtures of materials are used to construct the bed of roughened channels, ranging from fines such as sand, silt and gravel to coarse elements like cobbles and boulders. Fines are a critical element in limiting permeability of the constructed bed thus keeping stream flow at the surface of the roughened channel during low flow periods. This report discusses work of a research project designed to discover factors that are key to successful long-term implementation of fish passageways, especially focused on the construction process. Areas of inquiry postulated in this study are that failures experienced in actual installations may be due to inadequate range and/or mix of soil and rock material gradation; unexpected water velocity, especially during high flows; inadequate mixing of rock and soil materials during construction; and inadequate compaction of rock and soil materials during construction. This report suggests that several factors may be especially important considerations in fish passage success. These factors are the relationship of downstream slope to structure slope, well-graded fine soil materials in the channel fill (improved by choice of fill source), and frequent site visits. Improving fish passages for cost-efficient fish movement is a priority for government agencies such as Oregon Department of Transportation (ODOT) and Oregon Transportation Research and Education Consortium (OTREC). Close